Gymrek Melissa, Erlich Yaniv
Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, MA, USA.
Methods Mol Biol. 2013;1038:113-35. doi: 10.1007/978-1-62703-514-9_7.
Short tandem repeats (STRs), also known as microsatellites, have a wide range of applications, including medical genetics, forensics, and population genetics. High-throughput sequencing has the potential to profile large numbers of STRs, but cumbersome gapped alignment and STR-specific noise patterns hamper this task. We recently developed an algorithm, called lobSTR, to overcome these challenges and to accurately profile STRs from short reads. Here we describe how to use lobSTR to call STR variations from high-throughput sequencing datasets and to diagnose the quality of the calls.
短串联重复序列（STR），也被称为微卫星，有着广泛的应用，包括医学遗传学、法医学和群体遗传学。高通量测序有潜力对大量的STR进行分型，但是繁琐的缺口比对和STR特异性噪声模式阻碍了这项任务。我们最近开发了一种名为lobSTR的算法，以克服这些挑战并从短读段中准确地对STR进行分型。在这里，我们描述如何使用lobSTR从高通量测序数据集中调用STR变异并诊断调用的质量。
